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Novel Anthropometry-Based Calculation of the Body Heat Capacity in the Korean Population.

Pham DD, Lee JH, Lee YB, Park ES, Kim KY, Song JY, Kim JE, Leem CH - PLoS ONE (2015)

Bottom Line: Four different HCs were calculated and compared using a weight-based HC (HC_Eq1), two HCs estimated from fat and fat-free mass (HC_Eq2 and HC_Eq3), and an HC calculated from fat, protein, water, and mineral mass (HC_Eq4).In conclusion, our results suggest that gender, BSA, and weight are the independent factors for calculating HC.For the first time, a predictive equation based on anthropometry data was developed and this equation could be useful for estimating HC in the general Korean population without body-composition measurement.

View Article: PubMed Central - PubMed

Affiliation: Department of Physiology, University of Ulsan College of Medicine, 88 OlympicRo 43-gil Songpa-gu, Seoul, Republic of Korea.

ABSTRACT
Heat capacity (HC) has an important role in the temperature regulation process, particularly in dealing with the heat load. The actual measurement of the body HC is complicated and is generally estimated by body-composition-specific data. This study compared the previously known HC estimating equations and sought how to define HC using simple anthropometric indices such as weight and body surface area (BSA) in the Korean population. Six hundred participants were randomly selected from a pool of 902 healthy volunteers aged 20 to 70 years for the training set. The remaining 302 participants were used for the test set. Body composition analysis using multi-frequency bioelectrical impedance analysis was used to access body components including body fat, water, protein, and mineral mass. Four different HCs were calculated and compared using a weight-based HC (HC_Eq1), two HCs estimated from fat and fat-free mass (HC_Eq2 and HC_Eq3), and an HC calculated from fat, protein, water, and mineral mass (HC_Eq4). HC_Eq1 generally produced a larger HC than the other HC equations and had a poorer correlation with the other HC equations. HC equations using body composition data were well-correlated to each other. If HC estimated with HC_Eq4 was regarded as a standard, interestingly, the BSA and weight independently contributed to the variation of HC. The model composed of weight, BSA, and gender was able to predict more than a 99% variation of HC_Eq4. Validation analysis on the test set showed a very high satisfactory level of the predictive model. In conclusion, our results suggest that gender, BSA, and weight are the independent factors for calculating HC. For the first time, a predictive equation based on anthropometry data was developed and this equation could be useful for estimating HC in the general Korean population without body-composition measurement.

No MeSH data available.


Related in: MedlinePlus

Correlation between the heat capacity (HC) values estimated by four equations.HC_Eq1, heat capacity calculated based on the widely used average specific heat capacity (0.83 kcal·kg-1·°C-1) and body weight; HC_Eq2, heat capacity calculated based on Minard’s equation; HC_Eq3, heat capacity calculated based on Havenith’s equation; and HC_Eq4, heat capacity calculated based on four components model. r, correlation coefficient; Diff, mean and 95% confident interval of difference between the two HC values (x—y).
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pone.0141498.g001: Correlation between the heat capacity (HC) values estimated by four equations.HC_Eq1, heat capacity calculated based on the widely used average specific heat capacity (0.83 kcal·kg-1·°C-1) and body weight; HC_Eq2, heat capacity calculated based on Minard’s equation; HC_Eq3, heat capacity calculated based on Havenith’s equation; and HC_Eq4, heat capacity calculated based on four components model. r, correlation coefficient; Diff, mean and 95% confident interval of difference between the two HC values (x—y).

Mentions: The training set included 282 men and 318 women, whereas the test set was composed of 138 men and 164 women. There was no difference in the demographic data, body composition characteristics or HC among the training and test sets (Table 1). Fig 1 shows the correlations between each HC equation. In general, the HC equations were well-correlated to each other. Equations based on two components (HC_Eq2 and HC_Eq3) and four components (HC_Eq4) were strongly correlated (r = 0.996 to 0.999, mean difference = 1.64 to 4.62 kcal·°C-1), whereas equations using the average SpHC for the whole body overestimated HC compared to the other equations (r = 0.974 to 0.990, mean difference = 1.66 to 6.28 kcal·°C-1). The difference among HCs estimated by equations based on body composition data was lowest between HC_Eq4 and HC_Eq2. Because HC_Eq4 originated from more precise body composition analysis, further analysis regarding the relationship between HC and the anthropometric indices was performed using HC_Eq4.


Novel Anthropometry-Based Calculation of the Body Heat Capacity in the Korean Population.

Pham DD, Lee JH, Lee YB, Park ES, Kim KY, Song JY, Kim JE, Leem CH - PLoS ONE (2015)

Correlation between the heat capacity (HC) values estimated by four equations.HC_Eq1, heat capacity calculated based on the widely used average specific heat capacity (0.83 kcal·kg-1·°C-1) and body weight; HC_Eq2, heat capacity calculated based on Minard’s equation; HC_Eq3, heat capacity calculated based on Havenith’s equation; and HC_Eq4, heat capacity calculated based on four components model. r, correlation coefficient; Diff, mean and 95% confident interval of difference between the two HC values (x—y).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4631517&req=5

pone.0141498.g001: Correlation between the heat capacity (HC) values estimated by four equations.HC_Eq1, heat capacity calculated based on the widely used average specific heat capacity (0.83 kcal·kg-1·°C-1) and body weight; HC_Eq2, heat capacity calculated based on Minard’s equation; HC_Eq3, heat capacity calculated based on Havenith’s equation; and HC_Eq4, heat capacity calculated based on four components model. r, correlation coefficient; Diff, mean and 95% confident interval of difference between the two HC values (x—y).
Mentions: The training set included 282 men and 318 women, whereas the test set was composed of 138 men and 164 women. There was no difference in the demographic data, body composition characteristics or HC among the training and test sets (Table 1). Fig 1 shows the correlations between each HC equation. In general, the HC equations were well-correlated to each other. Equations based on two components (HC_Eq2 and HC_Eq3) and four components (HC_Eq4) were strongly correlated (r = 0.996 to 0.999, mean difference = 1.64 to 4.62 kcal·°C-1), whereas equations using the average SpHC for the whole body overestimated HC compared to the other equations (r = 0.974 to 0.990, mean difference = 1.66 to 6.28 kcal·°C-1). The difference among HCs estimated by equations based on body composition data was lowest between HC_Eq4 and HC_Eq2. Because HC_Eq4 originated from more precise body composition analysis, further analysis regarding the relationship between HC and the anthropometric indices was performed using HC_Eq4.

Bottom Line: Four different HCs were calculated and compared using a weight-based HC (HC_Eq1), two HCs estimated from fat and fat-free mass (HC_Eq2 and HC_Eq3), and an HC calculated from fat, protein, water, and mineral mass (HC_Eq4).In conclusion, our results suggest that gender, BSA, and weight are the independent factors for calculating HC.For the first time, a predictive equation based on anthropometry data was developed and this equation could be useful for estimating HC in the general Korean population without body-composition measurement.

View Article: PubMed Central - PubMed

Affiliation: Department of Physiology, University of Ulsan College of Medicine, 88 OlympicRo 43-gil Songpa-gu, Seoul, Republic of Korea.

ABSTRACT
Heat capacity (HC) has an important role in the temperature regulation process, particularly in dealing with the heat load. The actual measurement of the body HC is complicated and is generally estimated by body-composition-specific data. This study compared the previously known HC estimating equations and sought how to define HC using simple anthropometric indices such as weight and body surface area (BSA) in the Korean population. Six hundred participants were randomly selected from a pool of 902 healthy volunteers aged 20 to 70 years for the training set. The remaining 302 participants were used for the test set. Body composition analysis using multi-frequency bioelectrical impedance analysis was used to access body components including body fat, water, protein, and mineral mass. Four different HCs were calculated and compared using a weight-based HC (HC_Eq1), two HCs estimated from fat and fat-free mass (HC_Eq2 and HC_Eq3), and an HC calculated from fat, protein, water, and mineral mass (HC_Eq4). HC_Eq1 generally produced a larger HC than the other HC equations and had a poorer correlation with the other HC equations. HC equations using body composition data were well-correlated to each other. If HC estimated with HC_Eq4 was regarded as a standard, interestingly, the BSA and weight independently contributed to the variation of HC. The model composed of weight, BSA, and gender was able to predict more than a 99% variation of HC_Eq4. Validation analysis on the test set showed a very high satisfactory level of the predictive model. In conclusion, our results suggest that gender, BSA, and weight are the independent factors for calculating HC. For the first time, a predictive equation based on anthropometry data was developed and this equation could be useful for estimating HC in the general Korean population without body-composition measurement.

No MeSH data available.


Related in: MedlinePlus